Mechanics of cooling liquids by forced evaporation in bubbles

Injecting a non-dissolvable gas into a saturated liquid results in sub-cooling of the liquid due to forced evaporation into the bubble. Previous studies assumed the rate of evaporation of liquid into the bubble to be independent of the degree of sub-cooling. In our study we quantify the bubble growth by direct observation using high speed imaging and prove that this hypothesis is not true. A phenomenological model of the bubble growth as a function of the degree of sub-cooling is developed and we find excellent agreement between the measurements and theory. This bubble cooling process is employed in cooling a liquid. By identification of all heat flows, we can well describe the cool down curve using bubble cooling. Bubble cooling provides an alternative cooling method for liquids without the use of complicated cooling techniques

Imputation of missing values of tumour stage in population-based cancer registration

<p>Abstract</p> <p>Background</p> <p>Missing data on tumour stage information is a common problem in population-based cancer registries. Statistical analyses on the level of tumour stage may be biased, if no adequate method for handling of missing data is applied. In order to determine a useful way to treat missing data on tumour stage, we examined different imputation models for multiple imputation with chained equations for analysing the stage-specific numbers of cases of malignant melanoma and female breast cancer.</p> <p>Methods</p> <p>This analysis was based on the malignant melanoma data set and the female breast cancer data set of the cancer registry Schleswig-Holstein, Germany. The cases with complete tumour stage information were extracted and their stage information partly removed according to a MAR missingness-pattern, resulting in five simulated data sets for each cancer entity. The missing tumour stage values were then treated with multiple imputation with chained equations, using polytomous regression, predictive mean matching, random forests and proportional sampling as imputation models. The estimated tumour stages, stage-specific numbers of cases and survival curves after multiple imputation were compared to the observed ones.</p> <p>Results</p> <p>The amount of missing values for malignant melanoma was too high to estimate a reasonable number of cases for each UICC stage. However, multiple imputation of missing stage values led to stage-specific numbers of cases of T-stage for malignant melanoma as well as T- and UICC-stage for breast cancer close to the observed numbers of cases. The observed tumour stages on the individual level, the stage-specific numbers of cases and the observed survival curves were best met with polytomous regression or predictive mean matching but not with random forest or proportional sampling as imputation models.</p> <p>Conclusions</p> <p>This limited simulation study indicates that multiple imputation with chained equations is an appropriate technique for dealing with missing information on tumour stage in population-based cancer registries, if the amount of unstaged cases is on a reasonable level.</p

Referral patterns of children with poor growth in primary health care

Background. To promote early diagnosis and treatment of short stature, consensus meetings were held in the mid nineteen nineties in the Netherlands and the UK. This resulted in guidelines for referral. In this study we evaluate the referral pattern of short stature in primary health care using these guidelines, comparing it with cut-off values mentioned by the WHO. Methods. Three sets of referral rules were tested on the

Three-dimensional coherent X-ray diffraction imaging of a ceramic nanofoam: determination of structural deformation mechanisms

Ultra-low density polymers, metals, and ceramic nanofoams are valued for their high strength-to-weight ratio, high surface area and insulating properties ascribed to their structural geometry. We obtain the labrynthine internal structure of a tantalum oxide nanofoam by X-ray diffractive imaging. Finite element analysis from the structure reveals mechanical properties consistent with bulk samples and with a diffusion limited cluster aggregation model, while excess mass on the nodes discounts the dangling fragments hypothesis of percolation theory.Comment: 8 pages, 5 figures, 30 reference

A phase I study of a new polyamine biosynthesis inhibitor, SAM486A, in cancer patients with solid tumours

Because tumour cell proliferation is highly dependent upon up-regulation of de-novo polyamine synthesis, inhibition of the polyamine synthesis pathway represents a potential target for anticancer therapy. SAM486A (CGP 48664) is a new inhibitor of the polyamine biosynthetic enzyme S-adenosylmethionine decarboxylase (SAMDC), more potent and specific than the first-generation SAMDC inhibitor methylglyoxal (bis) guanylhydrazone (MGBG). Preclinical testing confirmed promising antiproliferative activity. In this phase I study, SAM486A was given 4-weekly as a 120 h infusion. 39 adult cancer patients were enrolled with advanced/refractory disease not amenable to established treatments, PS ≤ 2, adequate marrow, liver, renal and cardiac function. Doses were escalated in 100% increments without toxicity in 24 pts from 3 mg m–2cycle–1up to 400 mg m–2cycle–1. At 550 and 700 mg m–2cycle–1reversible dose-limiting neutropenia occurred. Other toxicities included mild fatigue, nausea and vomiting. No objective remission was seen. Pharmakokinetic analysis showed a terminal half-life of approximately 2 days. AUC and Cmax were related to dose; neutropenia correlated with AUC. The recommended dose for further phase II studies on this schedule is 400 mg m–2cycle–1. © 2000 Cancer Research Campaig

Comparison of techniques for handling missing covariate data within prognostic modelling studies: a simulation study

Background: There is no consensus on the most appropriate approach to handle missing covariate data within prognostic modelling studies. Therefore a simulation study was performed to assess the effects of different missing data techniques on the performance of a prognostic model. Methods: Datasets were generated to resemble the skewed distributions seen in a motivating breast cancer example. Multivariate missing data were imposed on four covariates using four different mechanisms; missing completely at random (MCAR), missing at random (MAR), missing not at random (MNAR) and a combination of all three mechanisms. Five amounts of incomplete cases from 5% to 75% were considered. Complete case analysis (CC), single imputation (SI) and five multiple imputation (MI) techniques available within the R statistical software were investigated: a) data augmentation (DA) approach assuming a multivariate normal distribution, b) DA assuming a general location model, c) regression switching imputation, d) regression switching with predictive mean matching (MICE-PMM) and e) flexible additive imputation models. A Cox proportional hazards model was fitted and appropriate estimates for the regression coefficients and model performance measures were obtained. Results: Performing a CC analysis produced unbiased regression estimates, but inflated standard errors, which affected the significance of the covariates in the model with 25% or more missingness. Using SI, underestimated the variability; resulting in poor coverage even with 10% missingness. Of the MI approaches, applying MICE-PMM produced, in general, the least biased estimates and better coverage for the incomplete covariates and better model performance for all mechanisms. However, this MI approach still produced biased regression coefficient estimates for the incomplete skewed continuous covariates when 50% or more cases had missing data imposed with a MCAR, MAR or combined mechanism. When the missingness depended on the incomplete covariates, i.e. MNAR, estimates were biased with more than 10% incomplete cases for all MI approaches. Conclusion: The results from this simulation study suggest that performing MICE-PMM may be the preferred MI approach provided that less than 50% of the cases have missing data and the missing data are not MNAR

Role of age in presentation, response to therapy and outcome of autoimmune hepatitis

Background: Few studies with diverging results and a small sample size have compared autoimmune hepatitis (AIH) in the elderly to younger patients.Aim: To unbiasedly investigate the role of age in behaviour and treatment outcome of AIH.Methods: All patients with probable or definite AIH type 1 in four tertiary academic centres were included in this retrospective- and since 2006 prospective-cohort study. Influence of age on presentation, remission and outcome of AIH were investigated.Results: 359 patients were included. Presence of cirrhosis at AIH diagnosis around 30% was independent of age. ALAT was higher at age 30-60 years on AIH diagnosis, and above age 60 there were less acute onset, less jaundice and more concurrent autoimmune disease. Remission was reached in 80.2%, incomplete remission in 18.7%, only 1.1% (all aged 50-65) was treatment-refractory. Age was not an independent predictor of remission, while cirrhosis was. Above age 45 there was more diabetes, above age 60 more loss of remission. Rate of progression to cirrhosis was 10% in the 10 years after diagnosis and unrelated to age at AIH diagnosis. With onset below age 30, there was more development of decompensated cirrhosis over time. With higher age at AIH diagnosis there was a lower survival free of liver-related death or liver transplantation.Conclusions: AIH presents at all ages. Age influences features at diagnosis, but not response to treatment, while survival without liver-related death or liver transplantation decreases with higher age at diagnosis.</p